import pandas as pd
import numpy as np
import pyrsm as rsm
import matplotlib.pyplot as plt
import statsmodels.api as sm
import seaborn as snsI’m going to analyze customer flow and behavior at Rogers market, which uses Amazon’s Just Walk Out technology, for my MGTA 456 - Supply Chain Analytics course at UC San Diego, MSBA program. The project involves creating an inventory build-up diagram and examining customer entry and exit patterns to determine the number of customers in the store each minute, entries in 15-minute intervals, and the average shopping duration using Little’s Law. This will help identify peak shopping times and optimize store operations to improve the shopping experience.
What is Amazon’s Just Walk Out Technology?: Amazon’s “Just Walk Out” technology enables a shopping experience where customers can enter a store, pick up the items they want, and leave without the need to check out at a traditional cashier station. It enables to track the consumer’s shopping behavior and store the data
Data
Load the data into the enviorment
I will begin by loading the necessary packages in both Python and R to handle data manipulation and analysis. I will then explore and explain the variables within the dataset, detailing their types, purposes, and any noteworthy aspects.
library(tidyverse)
library(magrittr)
library(scales)
library(data.table)
library(reticulate)rogers <- read.csv("Rogers_022824.csv")rogers %>%
DT::datatable(
extensions = 'Buttons',
options = list(
dom = 'Blfrtip',
buttons = c('copy', 'csv', 'excel'),
pageLength = 5,
scrollX = TRUE
)
)